logisticAnalysis.py

#from sklearn.svm import SVC
#from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
from sklearn import cross_validation
from sklearn.pipeline import Pipeline
from temporalPivot import playByPlay
from sklearn.preprocessing import MinMaxScaler

from sklearn import linear_model


pbp = playByPlay()
'''
pbp.select("KC",2010)
preppedData = pbp.temporal(20)

logistic = linear_model.LogisticRegression(C=100, solver='newton-cg')

pipeline = Pipeline([('min/max scaler',MinMaxScaler(feature_range=(0.0, 1.0))),
                     ('logistic',logistic)])
print "Classifier created"

print "Train Classification report:"
pipeline.fit(preppedData['train'],preppedData['label'])

y_true, y_pred = preppedData['label'], pipeline.predict(preppedData['train'])
print classification_report(y_true,y_pred)
print accuracy_score(y_true,y_pred)

print "CrossValidation:"
scores = cross_validation.cross_val_score(pipeline,preppedData['train'],preppedData['label'],cv=10)
print scores.mean()

print "Test Classification report:"
pipeline.fit(preppedData['train'][:-300],preppedData['label'][:-300])

y_true, y_pred = preppedData['label'][-300:], pipeline.predict(preppedData['train'][-300:])
print classification_report(y_true,y_pred)
print accuracy_score(y_true,y_pred)

'''
def testRunner():
	#pipeline = Pipeline([
     #   ('min/max scaler', MinMaxScaler(feature_range=(0.0, 1.0))),
      #  ('svm',SVC(kernel='poly',C=100,degree=2))])
	  
	logistic = linear_model.LogisticRegression(C=100, solver='newton-cg')

	pipeline = Pipeline([('min/max scaler',MinMaxScaler(feature_range=(0.0, 1.0))),('logistic',logistic)])
	pbp.testingFrameworkByTeam(pipeline,year=2015)
testRunner()